Semi-Supervised Online Cross-Modal Hashing

Online cross-modal hashing has gained increasing interest due to its ability to encode streaming data and update hash functions simultaneously. Existing online methods often assume either fully supervised or completely unsupervised settings. However, they overlook the prevalent and challenging scenario of semi-supervised cross-modal streaming data, where diverse data types, including labeled/unlabeled, paired/unpaired, and multi-modal, are intertwined. To address this issue, we propose Semi-Supervised Online Cross-modal Hashing (SSOCH). It presents an alignment-free pseudo-labeling strategy that extracts semantic information from unlabeled streaming data without relying on pairing relations. Furthermore, we design an online tri-consistent preserving scheme, integrating pseudolabeled data regularization, discriminative label embedding, and fine-grained similarity preservation. This scheme fully explores consistency across data annotation, modalities, and streaming chunks, improving the model's adaptiveness in these challenging scenarios. Extensive experiments on benchmark datasets demonstrate the superiority of SSOCH under various scenarios, highlighting the importance of semi-supervised learning for online cross-modal hashing.